Unambiguous identification of N-containing oxygenated organic molecules using a chemical-ionization Orbitrap (CI-Orbitrap) in an eastern Chinese megacity

Oxygenated organic molecules (OOMs) are dominated by the N-containing species in polluted urban environments. As N-containing OOMs, especially those with more than one nitrogen atom, prevail in the high $m/z$ (mass-to-charge) range ($m/z\mathit{>}$ 350 Th), unambiguous identification of N-containing OOMs is highly desirable for understanding of their formation processes, precursors and influencing factors. To achieve this, we applied an ultra-high-resolution chemical-ionization Orbitrap (CI-Orbitrap) in a field campaign and found that OOMs contain one (1N-OOMs), two (2N-OOMs) and three (3N-OOMs) nitrogen atoms comprised 50 %, 26 % and 4 %, respectively, of total OOMs. More interestingly, the fraction of 2N-OOMs increased with the increase in carbon number (nC) and was dominated by the ones derived from aliphatic precursors (2N-OOM_Ali, 64.2 %), indicating the importance of multistep oxidation. Plausible precursors of 2N-OOMs were aliphatics (2N-OOM_Ali, 64.2 %), aromatics (2N-OOM_Aro, 16 %) and monoterpenes (2N-OOM_MT, 15.4 %). The absolute concentrations of 2N-OOMs were greatly affected by the pollution level for most cases. The 2N-OOM_Ali was the most abundant 2N-OOM, and its fraction even increased on the polluted day with an enhanced proportion of the ones with nC >10. While 2N-OOM_Ali and 2N-OOM_Aro were dominated by daytime photochemical production, nighttime NO₃-initiated oxidation played a comparable role to the daytime photochemistry in the formation of 2N-OOM_MT. The 2N-OOM_Aro species were of the highest oxygenation level, followed by 2N-OOM_MT and 2N-OOM_Ali, which were affected by photochemistry and NO_x concentrations. These results highlight the significant formation of 2N-OOMs and the influencing factors on their formation in polluted urban environments, where various volatile organic compound (VOC) precursors and atmospheric oxidants are present.

1 Introduction

Secondary organic aerosol (SOA) accounts for a significant fraction of particulate matters (Donahue et al., 2009; Ehn et al., 2014; Hallquist et al., 2009; Jimenez et al., 2009). Volatile organic compounds (VOCs) and their oxidation products, i.e., OVOCs, are important precursors of SOA in the atmosphere (Atkinson and Arey, 2003; Bianchi et al., 2019; Ehn et al., 2014; Nie et al., 2022). N-containing oxygenated organic molecules (OOMs) have been identified as important products upon VOC oxidation. Especially at high NO_x levels, these products become more dominant, while the others (i.e., alcohols, hydroperoxides and RO₂ cross-reaction products) are likely suppressed (Bianchi et al., 2019; Zhao et al., 2018). The nitrogen atoms in OOMs are assumed to be mainly associated with the nitrate group (−ONO₂) formed from bimolecular reaction between RO₂ radical and NO. Field measurements have also shown that up to 77 % of molecules in organic aerosol (OA) contain nitrate functional groups under different atmospheric conditions (Ditto et al., 2020; Kenagy et al., 2021; Kiendler-Scharr et al., 2016; Lee et al., 2016; Ng et al., 2017; Lin et al., 2021; Rollins et al., 2013; Xu et al., 2015; Ye et al., 2021; Yu et al., 2019).

The N-containing OOMs can be classified into 1N-OOMs, 2N-OOMs and 3N-OOMs, according to the number of N atoms in the molecule. The chemical composition of N-containing OOMs is determined by their precursors, formation pathways and NO_x level in the atmosphere (Bianchi et al., 2019; Ehn et al., 2014; Nie et al., 2022; Pye et al., 2019; Riva, 2016; Yan et al., 2016). Recent observations in megacities of China have indicated that 2N-OOMs account for significant fractions (about 30 %–33 %) among total N-containing OOMs besides 1N-OOMs (66 %–70 %) due to the high NO_x concentrations in polluted urban environments (Nie et al., 2022; Yan et al., 2021). Some laboratory studies have also proposed that the potential formation pathways of 2N-OOMs, such as multiple-step OH oxidation (Garmash et al., 2020) or NO₃-initiated oxidation followed by NO termination (Kiendler-Scharr et al., 2016; Liebmann et al., 2019), suggest the increased importance of multistep bimolecular oxidation in the formation of 2N-OOMs. On the other hand, it has also been found that the formation of 2N-OOMs shows a clear preference for specific precursors compared to 1N-OOMs, i.e., a significantly higher branch ratio of 2N-OOMs from aliphatic hydrocarbons compared to those from aromatics (Nie et al., 2022), suggesting a considerable difference from 1N-OOMs in terms of the formation pathway. Determining the formation pathway of N-containing OOMs, especially those containing two to three nitrogen atoms, in real atmosphere is challenging. Identification of their chemical compositions at the molecular level is key for advancing our understanding of the precursors, formation and sources of N-containing OOMs in polluted atmosphere, where thousands of oxidation products exist and evolve constantly.

Traditionally, a chemical-ionization atmospheric-pressure-interface time-of-flight mass spectrometer (CI-APi-TOF) has been used to measure gaseous OOMs (Berndt et al., 2016; Ehn et al., 2014; Jokinen et al., 2014; Rissanen et al., 2014). Using a CI-APi-TOF, an increasing number of studies have reported the formation of OOMs through the oxidation of various VOC precursors in chambers or flow tubes (Berndt et al., 2016, 2018; Ehn et al., 2014; Garmash et al., 2020; Jokinen et al., 2014, 2015; Rissanen et al., 2014; Wang et al., 2020; Zhao et al., 2018). While 2N-OOMs in real ambient atmosphere are almost exclusively located in a high $m/z$ (mass-to-charge) range (i.e., 300–500 Th), a CI-APi-TOF with the highest mass resolving power of 12 000 ($m/\mathrm{\Delta }m$, in full width at half maximum) at $m/z=\mathrm{200}$ Th and above can hardly identify the molecular compositions of 2N-OOMs unambiguously. This is because low mass resolving power imposes significant uncertainties onto separating overlapping peaks, which increase rapidly with increasing $m/z$ and decreasing mass resolving power. Taking the integer $m/z$ of 342 as an example, multiple peaks overlap at this nominal mass, i.e., C₇H₈O₁₀N₂(NO₃)⁻ (342.0057 Th), C₈H₁₂O₉N₂(NO₃)⁻ (342.0421 Th), C₉H₁₆O₈N₂(NO₃)⁻ (342.0785 Th) and C₁₀H₂₀O₇N₂(NO₃)⁻ (342.1149 Th). The adjacent peaks are of mass differences (m) of 0.0364, and a good peak separation of these peaks (4σ) requires mass resolving power of at least 16 000. Therefore, the development and application of mass spectrometry techniques with extremely high performance in terms of the detection limit, time resolution and mass resolving power are highly desirable.

To achieve accurate identification of the molecular formula from the extremely complex mass spectra, a CI (nitrate) inlet has also been coupled to an Orbitrap mass spectrometer (CI-Orbitrap) to measure the OOMs at ultra-high mass resolving power ($m/\mathrm{\Delta }m\mathit{>}\mathrm{100}\mathrm{000}$ at $m/z=$ 200 to 500 Th) (Riva et al., 2019a; Zhang et al., 2022). The ultra-high mass resolving power of CI-Orbitrap will undoubtedly provide significant improvements in molecular identification, separation and quantification. Herein, we applied a CI-Orbitrap in a field campaign for the measurements of OOMs, with a special focus on 2N-OOMs, at the molecular level in urban Shanghai. The site represents a typical eastern Chinese megacity characterized by intense human activity, multiple anthropogenic emissions and high NO_x concentrations. Based on the measurement results as well as our current knowledge on N-containing OOM formation, we classify the observed 2N-OOMs into different precursor groups and explore the potential influencing factors in their formation. Furthermore, supported by positive matrix factorization (PMF), sources and gas-phase oxidation processes for 2N-OOM formation in urban Shanghai are identified.

2 Ambient measurement and methodology

2.1 Measurements

The field campaign was carried out from 31 October to 18 November 2020 on the top floor of an eight-story building in the Shanghai Academy of Environmental Sciences (31^∘18^′ N, 121^∘43^′ E; Fig. S1), which sits in a densely populated region surrounded by commercial properties and residential dwellings without significant industrial sources nearby. The site can represent a typical urban area of Shanghai affected by severe local emissions from vehicular traffic and commercial and residential activities. Our campaign was carried out in autumn, which represents a typical transition period from strong photochemistry in summer to intense regional transport in winter. At times, air masses transported from the neighboring provinces or even further from northern China can also affect the air quality of the site.

The 2N-OOMs as well other OOMs were measured in real time with a nitrate Orbitrap. The operation of the nitrate Orbitrap has been detailed in previous studies as well as in one of our companion studies (Zhang et al., 2022); thus it is only briefly described here. Ambient air was drawn into the ionization source through a 1 m stainless-steel tube ($\mathrm{3}/\mathrm{4}$ in.). The reagent ion was produced by passing nitric acid in sheath flow (20 L min⁻¹) into a Photoionizer (Model L9491, Hamamatsu, Japan) and was then introduced into a co-axial laminar flow reactor, in which the reagent ions interact with the air samples. The charged species were detected by an Orbitrap mass analyzer with a mass resolving power of about 140 000. Mass-dependent transmission calibrations were also performed using a depletion method (Heinritzi et al., 2016). Other ancillary measurements, including of the PM_2.5 concentrations, trace gases (SO₂, O₃ and NO_x) and volatile organic compounds, as well as meteorological parameters (wind direction and speed, solar radiation, etc.), are detailed in the Supplement (Sect. S1). An overview of the measurement data, illustrating the air quality as well as the meteorological conditions during the campaign, is provided in Sect. S2 and Fig. S2.

2.2 Data analysis of nitrate CI-Orbitrap

The raw mass spectra were first extracted by Orbitool (Cai et al., 2020), and the molecular information was then achieved by applying a homemade toolkit based on MATLAB software. The toolkit drew on the idea from the “tofTools” package, which is used for analyzing the mass spectral data obtained from the TOF analyzer, such as a nitrate CI-API-TOF (Junninen et al., 2010). The concentrations of the detected species are then determined as follows:

$$\begin{array}{}\text{(1)}& \left[X\right]={\displaystyle \frac{i\left[X^{-}\right]}{{\mathrm{NO}}_{\mathrm{3}}^{-}\cdot {\left({\mathrm{HNO}}_{\mathrm{3}}\right)}_{\text{0–2}}}}\cdot C\end{array}$$

where i[X⁻] is the transmission-corrected signal intensity of ion X in units of counts per second (cps) and C represents the calibration factor. C is determined from the collision frequency of target species with the nitrate ions (cluster) during its residence in the charger, taking into account the losses onto the walls of the reactor and the tube (Eq. 2):

$$\begin{array}{}\text{(2)}& C=C_{{\mathrm{H}}_{\mathrm{2}}{\mathrm{SO}}_{\mathrm{4}}}={\displaystyle \frac{\mathrm{1}}{k_{\mathrm{ion}}\cdot \mathrm{RT}\cdot f_{\mathrm{inlet}}}},\end{array}$$

where k_ions is the ion collision frequency in the range of $\mathrm{1.7}\times {\mathrm{10}}^{-\mathrm{9}}$ to $\mathrm{2.3}\times {\mathrm{10}}^{-\mathrm{9}}$ cm³ s⁻¹ (Ehn et al., 2014), RT is the residence time in the charger and f_inlet represents the fractions of target species that passed through the inlet.

Herein, we apply the C determined for sulfuric acid (H₂SO₄) of 3.4×10⁹ molecules cm⁻³ ncps⁻¹ to semi-quantify the concentrations of OOMs, which has been widely used in previous studies (Ehn et al., 2014; Yan et al., 2021; Yao et al., 2018). Among the low-volatility vapors, it has been demonstrated that nitrate ions exhibit the highest charging efficiency toward H₂SO₄ (Ehn et al., 2014; Hyttinen et al., 2015, 2018; Riva et al., 2019b). The estimated concentrations of OOMs thus can be considered the lower limits with an uncertainty of ±50 % according to error propagation (Ehn et al., 2014). Positive matrix factorization (PMF) was also performed for the measured species using Source Finder (SoFi, v6.3) based on Igor and run by the multilinear engine (ME-2) as detailed in Sect. S3 and Figs. S3–S6 (Canonaco et al., 2013).

3 Results and discussion

3.1 Chemical characteristics of OOMs

In total, we have identified 562 OOMs, which were concentrated in the carbon number (nC) range of 5 to 10, taking up 84.6 % of total OOMs during the whole campaign (unless otherwise stated, all the reported values hereafter correspond to the average of the whole campaign). Possible precursors of C_5–10 OOMs include isoprenes (C₅), benzene/alkylbenzenes (C_6–10), aliphatic VOCs (C_5–10) and monoterpenes (C₁₀) according to previous studies (Bianchi et al., 2019; Nie et al., 2022). C_≤4 OOMs only took up a small fraction of 6.7 % among total OOMs and were likely a result of the decomposition from OOMs with large carbon numbers as suggested by one of our companion studies (Zhang et al., 2022). The remaining 8.7 % were C_>10 OOMs, which accounted for a dominating fraction (70 %, Fig. S7) among the extremely low-volatility organic compounds (ELVOCs, $C^{*}\mathit{<}\mathrm{3}\times {\mathrm{10}}^{-\mathrm{5}}$ µg m⁻³) based on a volatility parameterization proposed by Donahue and co-workers (Donahue et al., 2011, 2012; Schervish and Donahue, 2020) and potentially have larger impacts on SOA formation owing to their lower volatility.

We further classified the detected OOMs into four groups based on the number of N atoms they possessed, including non-nitrogen OOMs (0N-OOMs), 1N-OOMs, 2N-OOMs and 3N-OOMs. Their average fractional contributions to total OOM concentrations as well as the carbon number (nC) distributions are shown in Fig. 1. We found that 1N-OOMs dominated the total OOM concentration with an average fraction of 50 %, followed by 2N-OOMs (26 %), demonstrating the dominance of N-containing OOMs among total OOMs. The 3N-OOMs only took up a small fraction (4 %) of total OOMs, and the remaining 20 % were 0N-OOMs.

Figure 1 (a) Average mass spectrum of the detected OOMs during the whole campaign. The pie chart shows the fractions of OOMs with different numbers of nitrogen and carbon atoms; (b) the fractions of 0N-OOMs, 1N-OOMs, 2N-OOMs and 3N-OOMs among total OOMs as a function of the carbon number (nC).

More interestingly, we found that 1N-OOMs prevailed among the OOMs with nC ≤ 10, yet 2N-OOMs dominated the C_>10 OOMs (41.8 %–84.2 %), suggesting the increased importance of multistep bimolecular oxidation in the formation of 2N-OOMs with nC > 10. We also note that the fraction of 2N-OOMs increased stepwise with the increase in nC (Fig. 1b), while 3N-OOMs did not exhibit a similar dependence. The potential reason for this is that, with the increase in nC on the one hand, more active sites are potentially provided to promote the occurrence of multistep oxidation, but on the other hand, the potentially larger steric effect can hinder multistep oxidation. From our observation, these two factors lead to an overall positive coupling for 2N-OOMs but result in a non-monotonic trend for 3N-OOMs. Furthermore, these 2N-OOMs with nC > 10 had an average molecular composition of C_12.5H_22.7O_2.1(NO₃)₂. Assuming the nitrogen atoms are only associated with the nitrate group (−ONO₂), the mean double-bond equivalent (DBE) value (Nie et al., 2022; Xu et al., 2021) was 1.15 on the carbon skeleton, suggesting its origination from aliphatic compounds, such as alkanes or alkenes (Gong et al., 2005; Mentel et al., 2015; Wang and Hildebrandt Ruiz, 2018).

We thus further classified the 2N-OOMs into their possible VOC precursors following a recently developed workflow proposed by Nie and co-workers, which is based on the up-to-date understanding of VOC oxidation and molecular characters (i.e., number of different elements, DBE) as well as PMF results (Nie et al., 2022), i.e., aromatics (2N-OOM_Aro), aliphatics (2N-OOM_Ali) and monoterpenes (2N-OOM_MT). Note that we classify isoprene 2N-OOMs (2N-OOM_Iso) as 2N-OOM_Ali as well because of the low concentration of isoprene in the cold season. As a result, the average fractions of 2N-OOM_Aro, 2N-OOM_Ali and 2N-OOM_MT among total 2N-OOMs were 16.0 %, 64.2 % and 15.3 %, respectively (Fig. 2), suggesting significant contribution of aliphatic compounds to 2N-OOM formation. Taken together, the increased fraction of 2N-OOMs with the increase in nC and the dominant fraction of 2N-OOM_Ali highlight the significant contribution of high-molecular-weight aliphatic precursors (i.e., intermediate-volatility or semi-volatile organic compounds, I/SVOCs) to high-molecular-weight 2N-OOM formation and comprise potentially important SOA material. We thus focus our attention on the formation of 2N-OOMs in the following sections.

Figure 2 The time series of 2N-OOMs originating from different precursors. Four sub-periods were selected to further investigate the fractional distribution of different types of OOMs as shown in the pie charts, including a clean daytime case (12:00 to 14:00 LT on 4 November, PM_2.5=7.5 µg m⁻³, CL_day), a clean nighttime case (23:00 LT on 4 November to 01:00 LT on 5 November, PM_2.5=9.5 µg m⁻³, CL_night), a daytime case in a PM_2.5 episode (12:00 to 14:00 LT on 7 November, PM_2.5=44.0 µg m⁻³, PL_day) and a nighttime case in a PM_2.5 episode (23:00 LT on 7 November to 01:00 LT on 8 November, PM_2.5=60.5 µg m⁻³, PL_night). The sizes of pie charts are scaled to the total concentrations of 2N-OOMs.

3.2 2N-OOM formation in PM_2.5 episodes

To investigate the formation mechanisms and factors that may affect the 2N-OOM formation, 1 clean day (4–5 November) and 1 polluted day (7–8 November) based on the pollution levels, i.e., PM_2.5 concentrations, were selected for further analysis. Since OOM formation is directly mediated by photochemistry or nighttime chemistry, the clean and polluted cases were thus split into one clean daytime case (CL_day), one clean nighttime case (CL_night), one polluted daytime case (PL_day) and one polluted nighttime case (PL_night). Detailed information on durations, pollution levels, meteorological conditions and 2N-OOM concentrations during these four cases is summarized in Table 1.

Table 1 Summary of the four cases including the meteorological conditions and concentrations of trace gases and 2N-OOMs. Note that “ppt” denotes parts per trillion throughout this paper.

During the whole campaign, the concentrations of 2N-OOMs ranged from 1.1×10⁶ to 42.0×10⁶ molecules cm⁻³ as shown in Fig. 2. We found the concentrations of 2N-OOMs in the polluted cases were 1.7–2.7 times higher than those in clean cases. Table 1 further indicates that the absolute abundances of almost all 2N-OOM classes were higher during the polluted cases as compared to the clean cases no matter whether it was the daytime or nighttime, except for the daytime 2N-OOM_Aro. Specifically, 2N-OOM_Ali occupied the largest fractions, which were even higher in polluted cases (66 %–66 %) than those in clean cases (56 %–61 %, Fig. 2). Especially for the 2N-OOM_Ali with nC >10, its concentration in polluted cases increased by a factor of 2.3–4.8 compared to the clean cases (Fig. 3). From PMF analysis, we also identified a factor characterized by a series of 2N-OOM_Ali (i.e., C_nH_2n−2O₈N₂, n= 5 to 11) as the fingerprint molecules (Table S1). This factor tracks the PM_2.5 concentration well, especially during PM_2.5 episodes (Fig. S8), likely due to the availability of adequate aliphatic precursors during pollution episodes. Furthermore, 2N-OOM_Ali with nC >10 presented both higher concentrations and higher fractions during daytime cases compared to nighttime cases (Fig. 3), suggesting that the photochemical formation of 2N-OOM_Ali prevailed compared to nighttime formation. To compare CL_night and PL_night, it was also found that the pollution case would lead to the enhanced importance of nighttime formation pathways of 2N-OOM_Ali with nC >10.

Figure 3 The fractions of 2N-OOM_Ali with different carbon numbers in the four cases.

We note that the fraction of 2N-OOM_Ali increased during CL_night primarily due to the more evident decrease in 2N-OOM_Aro (Table 1), whose formation is dominated by photochemistry. On the other hand, the decrease in 2N-OOM_Aro concentrations during PL_night was not as obvious as those during CL_night. Due to the significant increase in 2N-OOM_Ali concentration, the fraction of 2N-OOM_Aro species decreased in pollution cases, but their absolute concentrations only underwent a few changes in the daytime. The 2N-OOM_MT species showed significantly higher concentrations but similar fractions in polluted cases. On the other hand, equivalent or even slightly higher concentrations during the nighttime compared to those in the daytime suggest the comparable importance of nighttime chemistry in 2N-OOM_MT formation in contrast to 2N-OOM_Ali and 2N-OOM_Aro, which will be discussed in later subsections.

To summarize, the absolute concentrations of 2N-OOM were greatly affected by the pollution level for most cases. Both the concentrations and the fractions of 2N-OOM_Ali were significantly promoted by pollution conditions, whereas the 2N-OOM_Aro species were predominantly affected by photochemical production, whose formation was less sensitive to pollution levels compared to 2N-OOM_Ali in the daytime. In contrast, the absolute concentrations of 2N-OOM_MT were also significantly influenced by pollution levels but seem not solely dependent on the daytime/nighttime formation pathway. In addition, we note that both daytime photochemistry and nighttime chemistry had profound effects on 2N-OOM formation at different pollution levels, presumably depending on availability of the precursors as well as the oxidants. We thus focus our attention on the formation of 2N-OOMs during the daytime versus nighttime in the following sections.

3.3 Daytime vs. nighttime formation of 2N-OOMs

We investigate the effects of photochemistry and nighttime chemistry on the formation of individual 2N-OOMs. While the former is dominated by OH radical oxidation, the latter involves NO₃ radical oxidation as well as reactions with ozone or other oxidants, e.g., halogen. Herein, we use solar radiation as a proxy for photochemical reactivity, and the concentrations of NO₃ radicals were estimated assuming that NO₃, NO₂ and N₂O₅ were under fast equilibration in the troposphere (Brown and Stutz, 2012). The correlation coefficients (Spearman type) between individual 2N-OOMs and solar radiation (R_{2N-OOMs-solar}) or NO₃ radicals ($R_{{\text{2N-OOMs-NO}}_{\mathrm{3}}}$) derived from different precursors during the whole campaign are shown in Fig. 4a. It should be noted that the concentrations of 2N-OOMs and NO₃ radicals were scaled with the boundary layer height before calculating the correlation coefficients here and below for correcting the effects of meteorological dilution.

Figure 4 (a) Statistical distribution of the correlation coefficients (Spearman type) between 2N-OOMs and solar radiation (R_{2N-OOMs-solar}) in red and the correlation coefficients between 2N-OOMs and [NO₃] ($R_{{\text{2N-OOMs-NO}}_{\mathrm{3}}}$) in blue for 2N-OOMs from different precursors. The horizontal lines are the median values, boxes denote the 25th- and 75th-percentile values, and whiskers represent the 10th- and 90th-percentile values. (b) The diel patterns of 2N-OOMs from different precursors.

Both 2N-OOM_Aro and 2N-OOM_Ali showed stronger correlations with solar radiation compared to NO₃ radicals, indicating their association with daytime photochemistry, since benzene/alkylbenzenes and aliphatic VOCs rapidly react with OH radicals compared to other oxidants, such as NO₃ radicals. This is also supported by the observation that both 2N-OOM_Aro and 2N-OOM_Ali peaked during noontime (12:00–14:00 LT) as shown in Fig. 4b. Similarly, the PMF analysis also distinguished two daytime factors. The daytime factor 1 peaked at around 12:00–14:00 LT (Table S1) and highly correlated with solar radiation (R=0.57). The fingerprint molecules of daytime factor 1 are C_nH_2n−4O₁₀N₂ (n= 8 to 10) with average DBE values of 2 on the carbon skeleton, suggesting the dominance of 2N-OOMs likely formed from aromatic precursors. Since each step of OH oxidation of aromatics followed by RO₂+NO_x termination would increase the hydrogen number (nH) by 1, this factor is likely dominated by 2N-OOMs formed from two steps of OH-initiated oxidation from alkylbenzenes given that the carbon numbers ranged from 8 to 10.

The key fingerprint molecule of daytime factor 2 is C_nH_2nO₈N₂ (n= 4 to 5) (accounting for 30.8 % in the factor profile), followed by C_nH_2n−2O₈N₂ (n= 5 to 6) (accounting for 9.7 % in the factor profile), which is likely a result of the decomposition from 2N-OOM_Ali species with large carbon numbers, according to their DBE values of 0–1 on the carbon skeleton. This aliphatic factor presented even higher correlation with solar radiation (R=0.65), peaking at around 12:00–14:00 LT. Strong daytime peaks together with the good correlations with irradiation suggest the dominance of photochemical oxidation in the formation of 2N-OOM_Ali. For 2N-OOM_Ali, although it showed strong a daytime peak, a weak nighttime peak was still observed. This indicates that although daytime formation of 2N-OOM_Ali prevails, 2N-OOM_Ali nighttime formation still existed. For example, we have obtained a nighttime factor from PMF analysis (nighttime factor 2), whose fingerprint molecules are C₅H₈O₉N₂ and C_nH_2nO₇N₂ (n= 5 to 8). C₅H₈O₉N₂ likely originated from isoprene, and C_nH_2nO₇N₂ was likely from anthropogenic aliphatic precursors.

Nighttime chemistry plays a more important role in the formation of 2N-OOM_MT. This is further supported by the slightly stronger correlation between 2N-OOM_MT and NO₃ radicals compared to solar radiation. For some specific 2N-OOM_MT species, the formation is likely a result of NO₃-radical-initiated oxidation. As shown in Fig. 5, we have identified a series of 2N-OOM_MT molecules with a molecular composition of C₁₀H₁₆O${}_{\mathrm{7},\mathrm{9},\mathrm{11}}$N₂, which showed strong positive correlations with NO₃ radicals. The occurrence of the propagation reaction from RO₂ to RO was critical to the formation of odd oxygen as proposed in previous chamber studies (Boyd et al., 2015; Claflin and Ziemann, 2018). Furthermore, under the nighttime conditions observed in urban Shanghai (Table 1), it is estimated that monoterpenes primarily react with NO₃, and the fate of nighttime RO₂'s is dominated by NO, which is clearly different from rural environments where NO levels likely drop to near zero after sunset (Romer et al., 2016) and RO₂'s are likely terminated by NO₃–RO₂ cross-reactions (Bates et al., 2022). Therefore, the formation of C₁₀H₁₆O${}_{\mathrm{7},\mathrm{9},\mathrm{11}}$N₂ likely started with the reaction of monoterpenes with NO₃ radicals forming a NO₃–C₁₀H₁₆ alkyl radical, followed by the formation of organic peroxy radicals (RO₂) upon addition of O₂. The RO₂ is then converted to an alkoxy radical (RO) upon reaction with NO. The autoxidation process would then start and introduce O₂ into the molecule stepwise, forming a series of more oxygenated RO₂ radicals, i.e., NO₃–C₁₀H₁₆(O)(OO)_n. The NO termination reaction of these RO₂ radicals would finally result in ONs with a chemical composition of NO₃–C₁₀H₁₆(O)(OO)_nO(NO)O (n= 0, 1, 2).

Figure 5 Scatterplot of $R_{\text{2N-OOMs-}{\mathrm{NO}}_{\mathrm{3}}}$ against R_{2N-OOMs-solar} for specific 2N-OOM species.

On the other hand, the reaction rate between monoterpenes (i.e., α-pinene, β-pinene and limonene) and NO₃ is about 60 000–140 000 times faster than that between monoterpenes and O₃ at 293 K (Master Chemical Mechanism Version 3.3.1, MCMv3.3.1), but the averaged nighttime concentration of O₃ (22.8 ppb) was only about 18 000 times higher than that of NO₃ (1.3 ppt). Therefore, the NO₃-initiated oxidation process had significant impacts on 2N-OOM_MT formation during the nighttime. The 2N-OOM_MT resulting from NO₃ oxidation is also resolved as a nighttime factor (nighttime factor 1) from PMF analysis, which tracked the NO₃ concentrations well (Fig. S9, R=0.46) and peaked at around 19:00–23:00 LT. The fingerprint molecule of nighttime factor 1 mainly included C₁₀H₁₆O₉N₂ and C₁₀H₁₆O₈N₂, which are generated from NO₃-initiated oxidation followed by NO termination, and this process will not change the nH of the parent monoterpene molecule.

3.4 Oxygenation level of 2N-OOMs

We then calculated the average effective oxygen number (nO_eff= nO − 2nN) of 2N-OOMs, which is used to indicate the oxidation state of carbon by excluding the oxygen atoms bonded with nitrogen atoms. Note that calculation of nO_eff assumes that the nitrogen atoms are only associated with the nitrate group (−ONO₂), which is reasonable after excluding nitrophenol peaks. The average nO_eff of 2N-OOMs from different precursors in CL_day, CL_night, PL_day and PL_night are shown in Fig. 6 and summarized in Table S2. The 2N-OOM_Aro had the highest nO_eff (4.8–5.6), followed by 2N-OOM_MT (4.5–4.9), and 2N-OOM_Ali had the lowest nO_eff (3.9–4.0). Difference in the oxygenation level of different types of OOMs can be attributed to the difference in oxidation mechanisms of the initiation reactions. For example, the OH-initiated oxidation of alkanes, aromatics and monoterpenes/alkenes would form a C_xH_yO₂ radical, C_xH_yO₅ radical and C_xH_yO₃ radical, respectively, incorporating different numbers of oxygen atoms into the original precursor molecules at the first step of oxidation (Master Chemical Mechanism Version 3.3.1, MCMv3.3.1). On the other hand, during the multiple-step oxidation in the daytime, aromatics could still provide more C=C bonds than other precursors after the initial step, which are plausibly capable of further reacting with OH, O₃ and other oxidants.

Figure 6 The nO_eff of 2N-OOMs derived from different precursors in the four cases; the error bars represent the standard deviations.

Furthermore, we also found that regardless of the pollution level, the nO_eff was considerably higher in daytime cases than in nighttime cases, particularly for 2N-OOM_Aro and 2N-OOM_MT, suggesting a profound effect of photochemistry on the formation of highly oxygenated 2N-OOMs. This is likely because of the high NO_x concentrations during the nighttime (Table 1), which could efficiently suppress the RO₂ radicals from autoxidation reactions, forming overall less oxygenated OOMs. The effect of NO_x on oxygenation levels will be discussed in a subsequent paragraph. The average nO_eff values of 2N-OOM_Ali in four sub-periods were similar without significant daytime and nighttime difference, ranging from 3.9–4.0. This could be partly explained by the fact that reactions with oxidants such as OH and halogen radicals will similarly result in the addition of oxygen atoms by 2 for alkanes during the first step of oxidation. Thus, the oxygenation levels of 2N-OOM_Ali were assumed to be insensitive to the oxidants in the daytime or nighttime.

It is known that NO is also critical in determining the fate of RO₂ radicals during oxidation, forming RO radicals or organonitrates. Formation of RO radicals and of organonitrates will have opposite effects on the oxidation state of the termination products, since the former will significantly increase the oxygenation state of carbon through initiating propagation reactions before termination. We thus explore the effects of NO as well as the total NO_x concentrations on the average oxygenation levels of 2N-OOMs from different precursors during the whole campaign (Fig. 7). Consistently with previous studies in polluted urban environments (Qiao et al., 2021; Yan et al., 2021), the detected 2N-OOMs were also of low oxygenation with nO_eff values of 3.9–5.4 (25th–75th percentile) compared to those measured in forests or in laboratory studies (Berndt et al., 2016; Ehn et al., 2014; Jokinen et al., 2014; Rissanen et al., 2014; Yan et al., 2016). The nO_eff of 2N-OOM_Aro and 2N-OOM_MT increased with the decrease in NO or NO_x concentrations. This is likely due to the prevalence of NO termination reactions because the maximum autoxidation rate constant of alkylbenzenes with long-chain substituents (e.g., isopropylbenzene, ethylbenzene) and monoterpenes is comparable to the bimolecular reaction rate between RO₂ and NO (Bianchi et al., 2019). The oxygenation levels of 2N-OOM_Ali appear to be insensitive to the pollution levels and NO or NO_x concentrations, which should be further investigated in future studies.

Figure 7 Effective oxygen number (nO_eff) of 2N-OOMs as a function of (a) NO concentration and (b) NO_x concentration. The colored squares represent the real measurements. The filled markers indicate the median values in the range as horizontal error bars show; the vertical error bars denote the 25th- and 75th-percentile values.

4 Conclusions

We report the unambiguous identification of 2N-OOMs as well as other OOMs using an ultra-high-resolution Orbitrap coupled with a nitrate inlet. We find that OOMs are distributed among a wide range of carbon numbers (nC = 4 to 16), among which the 2N-OOMs occupied a considerable fraction (26 %) of the total observed OOMs. During the whole campaign, the 2N-OOM concentrations ranged from 1.1×10⁶ to 42.0×10⁶ molecules cm⁻³ and were concentrated in the nC range of 5 to 10 with a high molecular weight ($m/z\mathit{>}\mathrm{350}$ Th), implying their low volatilities and thus potentially high contribution to local SOA formation.

Aliphatic, aromatics and monoterpenes were plausible precursors of 2N-OOMs with fractions of 64.2 %, 16 % and 15.4 %, respectively. The absolute concentrations of 2N-OOMs were greatly affected by the pollution level for most cases. The 2N-OOM_Ali was found to be the most abundant 2N-OOM, and its fraction even increased on the polluted day with an enhanced proportion of 2N-OOM_Ali species with nC >10, probably due to the high concentrations of aliphatic precursors accompanied by PM_2.5 episodes. A significant contribution of long-chain aliphatic compounds (nC >10) to 2N-OOM formation is also supported by the observation that the 2N-OOM fraction increased with the increase in nC and the species had low DBE values, likely through multistep bimolecular oxidation. The 2N-OOM_Ali and 2N-OOM_Aro mainly peaked in the daytime and showed stronger correlations with solar radiation over NO₃ radicals, indicating their association with daytime photochemistry, since benzene/alkylbenzenes and aliphatic hydrocarbons rapidly react with OH radicals compared with other oxidants, such as NO₃ radicals. In contrast, 2N-OOM_MT prevailed in both the daytime and the nighttime; some specific 2N-OOM_MT species showed strong positive correlations with NO₃ radicals and were likely a result of NO₃-radical-initiated oxidation, suggesting the comparable importance of nighttime NO₃ chemistry in 2N-OOM_MT formation. In terms of oxygenation levels, we found that 2N-OOM_Aro had the highest averaged nO_eff followed by 2N-OOM_MT. Daytime photochemistry and low NO_x concentrations had profound effects on the formation of more-oxygenated 2N-OOMs. The 2N-OOM_Ali had the lowest nO_eff and had negligible changes at different pollution levels. These results demonstrate the preference of 2N-OOM formation and the influencing factors in a Chinese megacity involving various VOC precursors (biogenic VOCs such as monoterpenes and anthropogenic VOCs such as aromatics and aliphatic hydrocarbons) and various atmospheric oxidants (such as OH radicals and NO₃ radicals) and highlight the influence of PM_2.5 episodes.